OpenRaiser/Intern-Atlas huggingface dataset:
Towards Understanding Man-on-the-Side Attacks (MotS) in SCADA Networks
Venue: arXiv cs.CR 2020
Year: 2020

We describe a new class of packet injection attacks called Man-on-the-Side Attacks (MotS), previously only seen where state actors have "compromised" a number of telecommunication companies. MotS injection attacks have not been widely investigated in scientific literature, despite having been discussed by news outlets and security blogs. MotS came to attention after the Edward Snowden revelations, which described large scale pervasive monitoring of the Internet's infrastructure. For an advanced adversary attempting to interfere with IT connected systems, the next logical step is to adapt this class of attack to a smaller scale, such as enterprise or critical infrastructure networks. MotS is a weaker form of attack compared to a Man-in-the-Middle (MitM). A MotS attack allows an adversary to read and inject packets, but not modify packets sent by other hosts. This paper presents practical experiments where we have implemented and performed MotS attacks against two testbeds: 1) on HTTP connections, by redirecting a victim to a host controlled by an adversary; and 2) on an Industrial Control network, where we inject falsified command responses to the victim. In both cases, the victims accept the injected packets without generating a suspiciously large number of unusual packets on the network. We then perform an analysis of three leading Network IDS to determine whether the attacks are detected, and discuss mitigation methods.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
Venue: arXiv cs.CL 2020
Year: 2020

Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
Big Fish, Little Fish, Critical Infrastructure: An Analysis of Phineas Fisher and the 'Hacktivist' Threat to Critical Infrastructure
Venue: arXiv cs.CR 2020
Year: 2020

The hacktivist threat actor is listed in many risk decision documents. Yet their tactics and techniques often remain a mystery. We create a MITRE ATT&CK (ATT&CK) model of a well known hacktivist who goes under the pseudonym of Phineas Fisher, and map that threat to critical infrastructure. The analysis is derived from hacker manifestos, journalist reporting, and official government documentation. This analysis fills a gap in current threat models, to better define what skills and methods a determined hacker might employ. This paper also identifies seven essential mitigations which can be deployed by critical infrastructure operations and asset owners, to prevent such intrusions by hacktivists. We are in the process of contributing this threat actor into the ATT&CK knowledge base.
Exploiting Social Networks. Technological Trends (Habilitation Thesis)
Venue: arXiv cs.SI 2020
Year: 2020

The habilitation thesis presents two main directions: 1. Exploiting data from social networks (Twitter, Facebook, Flickr, etc.) - creating resources for text and image processing (classification, retrieval, credibility, diversification, etc.); 2. Creating applications with new technologies : augmented reality (eLearning, games, smart museums, gastronomy, etc.), virtual reality (eLearning and games), speech processing with Amazon Alexa (eLearning, entertainment, IoT, etc.). The work was validated with good results in evaluation campaigns like CLEF (Question Answering, Image CLEF, LifeCLEF, etc.), SemEval (Sentiment and Emotion in text, Anorexia, etc.).
Learning from Sparse Datasets: Predicting Concrete's Strength by Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Despite enormous efforts over the last decades to establish the relationship between concrete proportioning and strength, a robust knowledge-based model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, data-driven machine learning (ML) methods offer a new solution to this problem. Although this approach is promising for handling the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, a major limitation of ML lies in the fact that large datasets are needed for model training. This is a concern as reliable, consistent strength data is rather limited, especially for realistic industrial concretes. Here, based on the analysis of a large dataset (>10,000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to "learn" how to reliably predict concrete strength as a function of the size of the dataset. Based on these results, we discuss the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.
What Happens To BERT Embeddings During Fine-tuning?
Venue: arXiv cs.CL 2020
Year: 2020

While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques (probing classifiers, Representational Similarity Analysis, and model ablations), we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes significant changes, it does not lead to catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning primarily affects the top layers of BERT, but with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI appear to involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.
Natural Hazards Twitter Dataset
Venue: arXiv cs.SI 2020
Year: 2020

With the development of the Internet, social media has become an important channel for posting disaster-related information. Analyzing attitudes hidden in these texts, known as sentiment analysis, is crucial for the government or relief agencies to improve disaster response efficiency, but it has not received sufficient attention. This paper aims to fill this gap by focusing on investigating attitudes towards disaster response and analyzing targeted relief supplies during disaster response. The contributions of this paper are fourfold. First, we propose several machine learning models for classifying public sentiment concerning disaster-related social media data. Second, we create a natural disaster dataset with sentiment labels, which contains nearly 50,00 Twitter data about different natural disasters in the United States (e.g., a tornado in 2011, a hurricane named Sandy in 2012, a series of floods in 2013, a hurricane named Matthew in 2016, a blizzard in 2016, a hurricane named Harvey in 2017, a hurricane named Michael in 2018, a series of wildfires in 2018, and a hurricane named Dorian in 2019). We are making our dataset available to the research community: https://github.com/Dong-UTIL/Natural-Hazards-Twitter-Dataset. It is our hope that our contribution will enable the study of sentiment analysis in disaster response. Third, we focus on extracting public attitudes and analyzing the essential needs (e.g., food, housing, transportation, and medical supplies) for the public during disaster response, instead of merely targeting on studying positive or negative attitudes of the public to natural disasters. Fourth, we conduct this research from two different dimensions for a comprehensive understanding of public opinion on disaster response, since disparate hazards caused by different types of natural disasters.
A Higher-order Trace Finite Element Method for Shells
Venue: arXiv cs.CE 2020
Year: 2020

A higher-order fictitious domain method (FDM) for Reissner-Mindlin shells is proposed which uses a three-dimensional background mesh for the discretization. The midsurface of the shell is immersed into the higher-order background mesh and the geometry is implied by level-set functions. The mechanical model is based on the Tangential Differential Calculus (TDC) which extends the classical models based on curvilinear coordinates to implicit geometries. The shell model is described by PDEs on manifolds and the resulting FDM may typically be called Trace FEM. The three standard key aspects of FDMs have to be addressed in the Trace FEM as well to allow for a higher-order accurate method: (i) numerical integration in the cut background elements, (ii) stabilization of awkward cut situations and elimination of linear dependencies, and (iii) enforcement of boundary conditions using Nitsche's method. The numerical results confirm that higher-order accurate results are enabled by the proposed method provided that the solutions are sufficiently smooth.
Interactive Video Stylization Using Few-Shot Patch-Based Training
Venue: arXiv cs.GR 2020
Year: 2020

In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are stylized according to the artist's intention. In contrast to previous style transfer techniques, our approach does not require any lengthy pre-training process nor a large training dataset. We demonstrate how to train an appearance translation network from scratch using only a few stylized exemplars while implicitly preserving temporal consistency. This leads to a video stylization framework that supports real-time inference, parallel processing, and random access to an arbitrary output frame. It can also merge the content from multiple keyframes without the need to perform an explicit blending operation. We demonstrate its practical utility in various interactive scenarios, where the user paints over a selected keyframe and sees her style transferred to an existing recorded sequence or a live video stream.
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
Venue: arXiv cs.CL 2020
Year: 2020

We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
Indirect Identification of Psychosocial Risks from Natural Language
Venue: arXiv cs.CL 2020
Year: 2020

During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based features were less useful for prediction of intimate partner violence, but moderately indirect multiple-choice questioning allowed for detection without explicit disclosure. Both methods may serve as an initial or complementary screening approach to detecting stigmatized risks.
RikiNet: Reading Wikipedia Pages for Natural Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor. The reader dynamically represents the document and question by utilizing a set of complementary attention mechanisms. The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner. On the Natural Questions (NQ) dataset, a single RikiNet achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks. To our best knowledge, it is the first single model that outperforms the single human performance. Furthermore, an ensemble RikiNet obtains 76.1 F1 and 61.3 F1 on long-answer and short-answer tasks, achieving the best performance on the official NQ leaderboard
An Axiomatic Approach to Existence and Liveness for Differential Equations
Venue: arXiv cs.LO 2020
Year: 2020

This article presents an axiomatic approach for deductive verification of existence and liveness for ordinary differential equations (ODEs) with differential dynamic logic (dL). The approach yields proofs that the solution of a given ODE exists long enough to reach a given target region without leaving a given evolution domain. Numerous subtleties complicate the generalization of discrete liveness verification techniques, such as loop variants, to the continuous setting. For example, ODE solutions may blow up in finite time or their progress towards the goal may converge to zero. These subtleties are handled in dL by successively refining ODE liveness properties using ODE invariance properties which have a complete axiomatization. This approach is widely applicable: several liveness arguments from the literature are surveyed and derived as special instances of axiomatic refinement in dL. These derivations also correct several soundness errors in the surveyed literature, which further highlights the subtlety of ODE liveness reasoning and the utility of an axiomatic approach. An important special case of this approach deduces (global) existence properties of ODEs, which are a fundamental part of every ODE liveness argument. Thus, all generalizations of existence properties and their proofs immediately lead to corresponding generalizations of ODE liveness arguments. Overall, the resulting library of common refinement steps enables both the sound development and justification of new ODE existence and of liveness proof rules from dL axioms. These insights are put into practice through an implementation of ODE liveness proofs in the KeYmaera X theorem prover for hybrid systems.
Plan-Space State Embeddings for Improved Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Robot control problems are often structured with a policy function that maps state values into control values, but in many dynamic problems the observed state can have a difficult to characterize relationship with useful policy actions. In this paper we present a new method for learning state embeddings from plans or other forms of demonstrations such that the embedding space has a specified geometric relationship with the demonstrations. We present a novel variational framework for learning these embeddings that attempts to optimize trajectory linearity in the learned embedding space. We show how these embedding spaces can then be used as an augmentation to the robot state in reinforcement learning problems. We use kinodynamic planning to generate training trajectories for some example environments, and then train embedding spaces for these environments. We show empirically that observing a system in the learned embedding space improves the performance of policy gradient reinforcement learning algorithms, particularly by reducing the variance between training runs. Our technique is limited to environments where demonstration data is available, but places no limits on how that data is collected. Our embedding technique provides a way to transfer domain knowledge from existing technologies such as planning and control algorithms, into more flexible policy learning algorithms, by creating an abstract representation of the robot state with meaningful geometry.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Venue: arXiv cs.CL 2020
Year: 2020

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural idea of an ensemble model of the two, existing ensemble methods only focused on leveraging one approach to enhance another, we argue however that they can be further mutually enhanced with a proper training strategy. In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario. It consists of a language-model-like generator, a ranker generator, and one ranker discriminator. Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts. The experimental results on a large short-text conversation data demonstrate the effectiveness of the ensembleGAN by the amelioration on both human and automatic evaluation metrics.
Query-level Early Exit for Additive Learning-to-Rank Ensembles
Venue: arXiv cs.IR 2020
Year: 2020

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers to investigate algorithms to make faster the traversal of the additive ensemble or to early terminate the evaluation of documents that are unlikely to be ranked among the top-k. In this paper, we investigate the novel problem of \textit{query-level early exiting}, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble. Besides the obvious advantage on query latency and throughput, we address the possible positive impact of query-level early exiting on ranking effectiveness. To this end, we study the actual contribution of incremental portions of the tree ensemble to the ranking of the top-k documents scored for a given query. Our main finding is that queries exhibit different behaviors as scores are accumulated during the traversal of the ensemble and that query-level early stopping can remarkably improve ranking quality. We present a reproducible and comprehensive experimental evaluation, conducted on two public datasets, showing that query-level early exiting achieves an overall gain of up to 7.5% in terms of NDCG@10 with a speedup of the scoring process of up to 2.2x.
NUBIA: NeUral Based Interchangeability Assessor for Text Generation
Venue: arXiv cs.CL 2020
Year: 2020

We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA which outperforms metrics currently used to evaluate machine translation, summaries and slightly exceeds/matches state of the art metrics on correlation with human judgement on the WMT segment-level Direct Assessment task, sentence-level ranking and image captioning evaluation. The model implemented is modular, explainable and set to continuously improve over time.
Design and Implementation of Air Selection based Augmented Reality Serious Game for Learning Capability Analysis
Venue: arXiv cs.GT 2020
Year: 2020

Rising advancements and ICT have changed the way of life of society, every single logical zone are exploiting innovation to get a genuine improvement. Specialists understand the advantages of utilizing genuine games as a dependable device in psychoanalyst. Hence, the exploration looks at important issues in regards to Dyspraxia issue in youngsters and presents a similar report in the treatments strategies by utilizing a non autonomous riddle and by utilizing the game, a Serious Game created in the intension of helping kids suffering from Dyspraxia to enhance their engine aptitudes and deftness through innovation. The investigation of information results indicated that exist a critical distinction among the two strategies, demonstrating that youngsters spending time with Serious Game got little schedule in the movement running and furthermore enhanced execution.
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning
Venue: arXiv cs.RO 2020
Year: 2020

Taking inspiration from how the brain coordinates multiple learning systems is an appealing strategy to endow robots with more flexibility. One of the expected advantages would be for robots to autonomously switch to the least costly system when its performance is satisfying. However, to our knowledge no study on a real robot has yet shown that the measured computational cost is reduced while performance is maintained with such brain-inspired algorithms. We present navigation experiments involving paths of different lengths to the goal, dead-end, and non-stationarity (i.e., change in goal location and apparition of obstacles). We present a novel arbitration mechanism between learning systems that explicitly measures performance and cost. We find that the robot can adapt to environment changes by switching between learning systems so as to maintain a high performance. Moreover, when the task is stable, the robot also autonomously shifts to the least costly system, which leads to a drastic reduction in computation cost while keeping a high performance. Overall, these results illustrates the interest of using multiple learning systems.